Subject of this contribution

The aim of this work is to build upon these good practices and provide additional guidance for the implementation of ML methods based on another use case, i.e. flooding prevention in a spinning band distillation and an extraction column. The main focus is set on an easy-to-follow procedure for the integration of ML solutions following the example of Min et al. , which has been described in the following in more detail. For a distillation column, sensor data is evaluated to forecast the pressure drop with supervised learning methods. Subsequently, the operating state is classified based on the forecast combined with a clustering algorithm to form an early flooding warning system. The distillation column is designed according to the MTP (module type package) concept, which provides easy access to all sensor data from OPC/UA servers via a Python script, demonstrating the advantages of standardized interfaces. Flooding in the extraction column is analyzed via computer-vision and a convolutional neural network to design smarter equipment that can identify its own operating state. Finally, an online control strategy based on the ML models is discussed and its usability within the MTP architecture is evaluated.